Existing computer-based applications independently and/or collectively operating in organizations are often non-adaptive or inadequately adaptive. These applications are most typically based on underlying hierarchical or other non-fuzzy network-based structures, and such structures offer limited capacity for automatic adaptation over time. The level of investment and commitment to non-adaptive systems often makes it difficult to justify immediately and/or completely converting to more adaptive systems. Thus there is a need for transformational systems and methods that can transform non-adaptive systems or inadequately adaptive systems to adaptive systems, including the transformation of non-fuzzy structures to fuzzy network-based structures that have a greater capacity for ongoing adaptation.
Furthermore, existing computer-implemented recommender systems can provide personalized recommendations based on learning from behavioral histories. While personalized recommendations can clearly be beneficial, a common criticism of such personalization systems is that they inhibit beneficial serendipity due to their bias toward recommending items that are aligned with a relatively narrow set of inferred interest areas, thereby depriving the recommendation recipient of becoming aware of potentially personally valuable content outside these interest areas. Thus there is a need for a system and method that retains the advantages of personalization while promoting a greater degree of beneficial serendipity.